Rainmaker busca su primer Machine Learning Researcher para establecer el uso del aprendizaje automático en toda la empresa. Identificarás los problemas más valiosos, construirás modelos funcionales y colaborarás con ingenieros y científicos para convertir la investigación en sistemas operativos. El rol es hands-on y de contribuidor individual, con foco en aplicaciones como predicción de agua líquida superenfriada, asimilación de datos y mejora de retrievals. Se espera entregar un modelo útil en los primeros tres meses.

Responsibilities

Assess potential ML projects across Rainmaker and prioritize them by operational value, data readiness, technical tractability, and time to useful results.
Deliver an operationally useful model or prototype within your first three months rather than spending a quarter exclusively on infrastructure or roadmap development.
Build models for forecasting, nowcasting, retrievals, multimodal atmospheric-state estimation, simulation, intervention analysis, and other scientific or operational applications.
Develop methods for forecasting the occurrence, location, amount, and persistence of supercooled liquid water at scales relevant to cloud-seeding operations.
Combine public NWP, radar, satellite, microwave-sounder, aircraft, UAS, sounding, surface, and in-situ observations.
Build datasets, labels, baselines, evaluation metrics, and validation procedures for variables that public systems do not observe or optimize well.
Establish honest experimental comparisons and characterize calibration, uncertainty, generalization, and failure modes.
Work closely with meteorologists and atmospheric scientists to define targets, physical constraints, useful priors, and ground truth.
Write research-quality software and build prototypes that software engineers can help productionize when an approach proves valuable.
Use Rainmaker's compute budget deliberately, scaling experiments only when the problem, data, and baseline justify it.
Help Rainmaker learn from every operation, field campaign, new sensor, and intervention.
Communicate results and limitations clearly to scientists, engineers, operators, and company leadership.

Requirements

Evidence of exceptional ability in machine learning research and engineering, regardless of whether it was developed in academia, industry, independent work, or another technical field.
Strong command of modern machine-learning methods and practical experience training, evaluating, and debugging models.
Strong Python skills and experience with a modern ML framework such as PyTorch, JAX, or an equivalent system.
Ability to turn ambiguous problems into measurable targets, tractable experiments, credible baselines, and working prototypes.
Sound statistical judgment, including careful treatment of leakage, distribution shift, calibration, uncertainty, and small or biased datasets.
Ability to work with noisy, sparse, multimodal, spatial, or temporal data.
Willingness to select simple methods when they are sufficient and reserve complex models for problems where they create measurable value.
Comfort working directly with scientists and engineers from domains you may not initially know.
High agency, rapid learning, and a strong bias toward useful results.

Nice to have

Experience with weather, climate, remote sensing, geospatial data, scientific ML, robotics, autonomy, aerospace, state estimation, computer vision, physical systems, or another data-constrained scientific domain.
Experience with forecasting, sequence models, probabilistic models, generative models, representation learning, sensor fusion, or data assimilation.
Experience working with radar, satellite, microwave-sounder, image, trajectory, gridded, or in-situ sensor data.
Experience taking a research model into real user workflows or production in partnership with software engineers.
Experience designing data-collection or labeling strategies when the existing dataset is insufficient.
Familiarity with atmospheric science is valuable but not required.

Benefits

Significant stock options with high potential upside as an early-stage company
401(k) with employer matching
Full health coverage (medical, dental, and vision insurance)
Relocation assistance provided (if applicable)
Unlimited PTO
Paid parental leave for both parents
Lunch provided when working in-office and a fully stocked kitchenette
Free EV charging at the HQ